Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Real Time Visual Cues Extraction for Monitoring Driver Vigilance
ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Finding faces in cluttered scenes using random labeled graph matching
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
A Unified Learning Framework for Real Time Face Detection and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Viewer-Centered Object Recognition in Monkeys
Viewer-Centered Object Recognition in Monkeys
Data Mining Spontaneous Facial Behavior with Automatic Expression Coding
Verbal and Nonverbal Features of Human-Human and Human-Machine Interaction
Editorial: Special issue: eye detection and tracking
Computer Vision and Image Understanding - Special issue on eye detection and tracking
Towards a portable intelligent facial expression recognizer
Intelligent Decision Technologies
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Drowsy driver detection through facial movement analysis
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Some experiments in audio-visual speech processing
NOLISP'07 Proceedings of the 2007 international conference on Advances in nonlinear speech processing
Journal of Network and Computer Applications
A learning approach to hierarchical feature selection and aggregation for audio classification
Pattern Recognition Letters
Visual-context boosting for eye detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
A dynamic threshold approach for skin tone detection in colour images
International Journal of Biometrics
Pain monitoring: A dynamic and context-sensitive system
Pattern Recognition
An integrated approach for head gesture based interface
Applied Soft Computing
Lip detection using confidence-based adaptive thresholding
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP
International Journal of Computer Vision and Image Processing
Real-time eye-gaze estimation using a low-resolution webcam
Multimedia Tools and Applications
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We formulate a probabilistic model of image generation and derive optimal inference algorithms for finding objects and object features within this framework. The approach models images as a collage of patches of arbitrary size, some of which contain the object of interest and some of which are background. The approach requires development of likelihood-ratio models for object versus background generated patches. These models are learned using boosting methods. One advantage of the generative approach proposed here is that it makes explicit the conditions under which it is optimal. We applied the approach to the problem of finding faces and eyes on arbitrary images. Optimal inference under the proposed model works in real time and is robust to changes in lighting, illumination, and differences in facial structure, including facial expressions and eyeglasses. Furthermore, the system can simultaneously track the eyes and blinks of multiple individuals. Finally we reflect on how the development of perceptive systems like this may help advance our understanding of the human brain.